10.17863/CAM.12107
Farewell, Vernon
0000-0001-6704-5295
Long, DL
Tom, Brian
0000-0002-3335-9322
Yiu, Sean
Su, Li
0000-0003-0919-3462
Two-Part and Related Regression Models for Longitudinal Data.
Apollo - University of Cambridge Repository (staging)
2017
longitudinal data
marginal covariate effects
mixture distributions
random effects
two-part models
Apollo - University of Cambridge Repository (staging)
Apollo - University of Cambridge Repository (staging)
2017-03
Article
2326-8298
2326-831X
Statistical models that involve a two-part mixture distribution are applicable in a variety of situations. Frequently, the two parts are a model for the binary response variable and a model for the outcome variable that is conditioned on the binary response. Two common examples are zero-inflated or hurdle models for count data and two-part models for semicontinuous data. Recently, there has been particular interest in the use of these models for the analysis of repeated measures of an outcome variable over time. The aim of this review is to consider motivations for the use of such models in this context and to highlight the central issues that arise with their use. We examine two-part models for semicontinuous and zero-heavy count data, and we also consider models for count data with a two-part random effects distribution.
The authors acknowledge the following funding sources: MRC funding U015261167, MC\_UP\_1302/3, and National Institutes of Health (NIH) grant U54GM104942 (NIGMS).